Why is the Dependencies page empty?
The Dependencies page shows a graph of services traced by Jaeger and connections between them. When you are using
all-in-one binary with in-memory storage, the graph is calculated on-demand from all the traces stored in memory. However, if you are using a real distributed storage like Cassandra or OpenSearch/Elasticsearch, it is too expensive to scan all the data in the database to build the service graph. Instead, the Jaeger project provides “big data” jobs that can be used to extract the service graph data from traces:
- https://github.com/jaegertracing/spark-dependencies - the older Spark job that can be run periodically
- https://github.com/jaegertracing/jaeger-analytics - the new (experimental) streaming Flink jobs that run continuously and builds the service graph in smaller time intervals
Why do I not see any spans in Jaeger?
Please refer to the Troubleshooting guide.
Do I need to run jaeger-agent?
jaeger-agent is not always necessary. Jaeger client libraries can be configured to export trace data directly to
jaeger-collector. However, the following are the reasons why running
jaeger-agent is recommended:
- If we want Jaeger client libraries to send trace data directly to collectors, we must provide them with a URL of the HTTP endpoint. It means that our applications require additional configuration containing this parameter, especially if we are running multiple Jaeger installations (e.g. in different availability zones or regions) and want the data sent to a nearby installation. In contrast, when using the agent, the libraries require no additional configuration because the agent is always accessible via
localhost. It acts as a sidecar and proxies the requests to the appropriate collectors.
- The agent can be configured to enrich the tracing data with infrastructure-specific metadata by adding extra tags to the spans, such as the current zone, region, etc. If the agent is running as a host daemon, it will be shared by all applications running on the same host. If the agent is running as a true sidecar, i.e. one per application, it can provide additional functionality such as strong authentication, multi-tenancy (see this blog post), pod name, etc.
- Agents allow implementing traffic control to the collectors. If we have thousands of hosts in the data center, each running many applications, and each application sending data directly to the collectors, there may be too many open connections for each collector to handle. The agents can load balance this traffic with fewer connections.
What is the recommended storage backend?
The Jaeger team recommends OpenSearch/Elasticsearch as the storage backend over Cassandra, for the following reasons:
Cassandra is a key-value database, so it is more efficient for retrieving traces by trace ID, but it does not provide the same powerful search capabilities as OpenSearch. Effectively, the Jaeger backend implements the search functionality on the client side, on top of k-v storage, which is limited and may produce inconsistent results (see issue-166 for more details). OpenSearch does not suffer from these issues, resulting in better usability. OpenSearch can also be queried directly, e.g. from Kibana dashboards, and provide useful analytics and aggregations.
Based on past performance experiments we observed single writes to be much faster in Cassandra than OpenSearch, which might suggest that it may sustain higher write throughput. However, because the Jaeger backend needs to implement search capability on top of k-v storage, writing spans to Cassandra is actually subject to large write amplification: in addition to writing a record for the span itself, Jaeger performs extra writes for service name and operation name indexing, as well as extra index writes for every tag. In contrast, saving a span to OpenSearch is a single write, and all indexing takes place inside the OpenSearch node. As a result, the overall throughput to Cassandra is comparable with OpenSearch.
One benefit of Cassandra backend is simplified maintenance due to its native support for data TTL. In OpenSearch the data expiration is managed through index rotation, which requires additional setup (see Elasticsearch Rollover).
Why do Jaeger trace IDs look differently in Kafka and in the UI?
Under the hood, at the data model level, the Jaeger trace IDs are a sequence of 16 bytes. However, these 16 bytes can be represented in many different ways:
- in the UI, we historically represented them as hex-encoded strings, e.g.,
7e90c0eca22784ec7e90c0eca22784ec. These strings can be either 32 characters long when using the 128-bit IDs (as more common in OpenTelemetry), or 16 characters if the IDs are generated in the legacy 64-bit mode.
- in the domain model in the Jaeger backend code, we represent trace ID as a pair of unsigned 64-bit integers using big-endian encoding. This was done for efficiency because a byte slice in Go requires an extra memory allocation.
- in the original Thrift model we also represented them as a pair of unsigned 64-bit integers
- in the Protobuf model, the ID is represented as a byte sequence. When Protobuf is serialized as a binary payload, these bytes are transmitted as is. However, Protobuf also supports a JSON encoding, where byte sequences are serialized using base64 encoding. So if you configure collector-Kafka-ingester pipeline to use the JSON encoding, you will see trace IDs that look like
fpDA7KInhOx+kMDsoieE7A==. These can be converted to hex-encoded IDs that are recognized by the UI using online tools like https://base64.guru/converter/encode/hex.
Do I need to run multiple collectors?
Does having high availability of collector improve the overall system performance like decreasing the dropped span count and having the less outage for trace collection? Is it recommended? If yes, why?
These are the reasons to run multiple instances:
- Your clients send so much data that a single collector is not able to accept it fast enough.
- You want higher availability, e.g., when you do rolling restarts of collectors for upgrade, to have some instances still running and able to process inbound data.
These are NOT the reasons to run multiple instances:
- To avoid data loss. The collector drops data when the backend storage is not able to save it fast enough. Increasing the number of collectors, with more memory allocated to their internal queues, could provide a small, temporary relief, but does not remove the bottleneck of the storage backend.